Jain, S.; Geraci, J.; Ruda, H.E. Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis. Technologies2023, 11, 183.
Jain, S.; Geraci, J.; Ruda, H.E. Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis. Technologies 2023, 11, 183.
Jain, S.; Geraci, J.; Ruda, H.E. Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis. Technologies2023, 11, 183.
Jain, S.; Geraci, J.; Ruda, H.E. Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis. Technologies 2023, 11, 183.
Abstract
Image synthesis poses a challenging problem that researchers in computer vision and machine learning have been grappling with for several decades. Numerous machine learning techniques have emerged and proven effective in generating high-fidelity artificial images. This study breaks new ground by exploring image synthesis through generative learning using the D-Wave 2000Q quantum annealer, marking the first attempt to address the issue of generative image synthesis on a Quantum Processing Unit (QPU). Alongside executing image synthesis on the quantum annealer, this research also compares its performance with existing classical models and delves into resolving the Generative Learning Trilemma.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
